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Article: Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities.

Lee, Jungwook / Kim, Daeseung / Xu, Xuanang / Kuang, Tianshu / Gateno, Jaime / Yan, Pingkun

medRxiv : the preprint server for health sciences

2023

Abstract: Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial ... ...

Abstract	Orthognathic surgery traditionally focuses on correcting skeletal abnormalities and malocclusion, with the expectation that an optimal facial appearance will naturally follow. However, this skeletal-driven approach can lead to undesirable facial aesthetics and residual asymmetry. To address these issues, a soft-tissue-driven planning method has been proposed. This innovative method bases bone movement estimates on the targeted ideal facial appearance, thus increasing the surgical plan's accuracy and effectiveness. This study explores the initial phase of implementing a soft-tissue-driven approach, simulating the patient's optimal facial look by repositioning deformed facial landmarks to an ideal state. The algorithm incorporates symmetrization and weighted optimization strategies, aligning projected optimal landmarks with standard cephalometric values for both facial symmetry and form, which are integral to facial aesthetics in orthognathic surgery. It also includes regularization to preserve the patient's original facial characteristics. Validated using retrospective analysis of data from both preoperative patients and normal subjects, this approach effectively achieves not only facial symmetry, particularly in the lower face, but also a more natural and normalized facial form. This novel approach, aligning with soft-tissue-driven planning principles, shows promise in surpassing traditional methods, potentially leading to enhanced facial outcomes and patient satisfaction in orthognathic surgery.
Language	English
Publishing date	2023-12-14
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.13.23299919
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Article ; Online: Machine Learning Effectively Diagnoses Mandibular Deformity Using Three-Dimensional Landmarks.

Xu, Xuanang / Deng, Hannah H / Kuang, Tianshu / Kim, Daeseung / Yan, Pingkun / Gateno, Jaime

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons

2023 Volume 82, Issue 2, Page(s) 181–190

Abstract: Background: Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions. Therefore, we hypothesized that ... ...

Abstract	Background: Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions. Therefore, we hypothesized that a multilayer perceptron (MLP) could accurately diagnose jaw deformities in three dimensions (3D). Purpose: Examine the hypothesis by focusing on anomalous mandibular position. We aimed to: (1) create a machine learning model to diagnose mandibular retrognathism and prognathism; and (2) compare its performance with traditional cephalometric methods. Study design, setting, sample: An in-silico experiment on deidentified retrospective data. The study was conducted at the Houston Methodist Research Institute and Rensselaer Polytechnic Institute. Included were patient records with jaw deformities and preoperative 3D facial models. Patients with significant jaw asymmetry were excluded. Predictor variables: The tests used to diagnose mandibular anteroposterior position are: (1) SNB angle; (2) facial angle; (3) mandibular unit length (MdUL); and (4) MLP model. Main outcome variable: The resultant diagnoses: normal, prognathic, or retrognathic. Covariates: None. Analyses: A senior surgeon labeled the patients' mandibles as prognathic, normal, or retrognathic, creating a gold standard. Scientists at Rensselaer Polytechnic Institute developed an MLP model to diagnose mandibular prognathism and retrognathism using the 3D coordinates of 50 landmarks. The performance of the MLP model was compared with three traditional cephalometric measurements: (1) SNB, (2) facial angle, and (3) MdUL. The primary metric used to assess the performance was diagnostic accuracy. McNemar's exact test tested the difference between traditional cephalometric measurement and MLP. Cohen's Kappa measured inter-rater agreement between each method and the gold standard. Results: The sample included 101 patients. The diagnostic accuracy of SNB, facial angle, MdUL, and MLP were 74.3, 74.3, 75.3, and 85.2%, respectively. McNemar's test shows that our MLP performs significantly better than the SNB (P = .027), facial angle (P = .019), and MdUL (P = .031). The agreement between the traditional cephalometric measurements and the surgeon's diagnosis was fair. In contrast, the agreement between the MLP and the surgeon was moderate. Conclusion and relevance: The performance of the MLP is significantly better than that of the traditional cephalometric measurements.
MeSH term(s)	Humans ; Prognathism/diagnostic imaging ; Retrognathia/diagnostic imaging ; Retrospective Studies ; Mandible/diagnostic imaging ; Mandible/abnormalities ; Malocclusion, Angle Class III/surgery ; Jaw Abnormalities ; Cephalometry/methods
Language	English
Publishing date	2023-11-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	392404-x
ISSN	1531-5053 ; 0278-2391
ISSN (online)	1531-5053
ISSN	0278-2391
DOI	10.1016/j.joms.2023.11.002
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Book ; Online: Federated Multi-organ Segmentation with Inconsistent Labels

Xu, Xuanang / Deng, Hannah H. / Gateno, Jaime / Yan, Pingkun

2022

Abstract: Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label ... ...

Abstract	Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label consistency across clients by the existing methods largely narrows its application scope. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. Incorporating such partially labeled data into a unified federation is an unexplored problem with clinical significance and urgency. This work tackles the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net) is proposed to extract organ-specific features through different encoding sub-networks. Each sub-network can be seen as an expert of a specific organ and trained for that client. Moreover, to encourage the organ-specific features extracted by different sub-networks to be informative and distinctive, we regularize the training of the MENU-Net by designing an auxiliary generic decoder (AGD). Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods. Source code is publicly available at https://github.com/DIAL-RPI/Fed-MENU. Comment: v1: 10 pages, 5 figures; v2: 14 pages, 5 figures, accepted by IEEE Transactions on Medical Imaging (TMI), published version available at https://doi.org/10.1109/TMI.2023.3270140, source code available at https://github.com/DIAL-RPI/Fed-MENU
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-06-14
Publishing country	us
Document type	Book ; Online
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Article ; Online: Correspondence attention for facial appearance simulation.

Fang, Xi / Kim, Daeseung / Xu, Xuanang / Kuang, Tianshu / Lampen, Nathan / Lee, Jungwook / Deng, Hannah H / Liebschner, Michael A K / Xia, James J / Gateno, Jaime / Yan, Pingkun

Medical image analysis

2024 Volume 93, Page(s) 103094

Abstract: In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element ... ...

Abstract	In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.
MeSH term(s)	Humans ; Face/diagnostic imaging ; Biomechanical Phenomena ; Computer Simulation ; Movement
Language	English
Publishing date	2024-01-26
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2024.103094
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Article: Improving Image Segmentation with Contextual and Structural Similarity.

Chen, Xiaoyang / Liu, Qin / Deng, Hannah H / Kuang, Tianshu / Lin, Henry Hung-Ying / Xiao, Deqiang / Gateno, Jaime / Xia, James J / Yap, Pew-Thian

Pattern recognition

2024 Volume 152

Abstract: Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically ... ...

Abstract	Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically inconsistent predictions. Here, we propose a contextual similarity loss (CSL) and a structural similarity loss (SSL) to explicitly and efficiently incorporate inter-voxel relationships for improved performance. The CSL promotes consistency in predicted object categories for each image sub-region compared to ground truth. The SSL enforces compatibility between the predictions of voxel pairs by computing pair-wise distances between them, ensuring that voxels of the same class are close together whereas those from different classes are separated by a wide margin in the distribution space. The effectiveness of the CSL and SSL is evaluated using a clinical cone-beam computed tomography (CBCT) dataset of patients with various craniomaxillofacial (CMF) deformities and a public pancreas dataset. Experimental results show that the CSL and SSL outperform state-of-the-art regional loss functions in preserving segmentation semantics.
Language	English
Publishing date	2024-04-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1466343-0
ISSN	0031-3203
ISSN	0031-3203
DOI	10.1016/j.patcog.2024.110489
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Article ; Online: Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive Medical Image Segmentation.

Chen, Xu / Kuang, Tianshu / Deng, Hannah / Fung, Steve H / Gateno, Jaime / Xia, James J / Yap, Pew-Thian

IEEE transactions on medical imaging

2022 Volume 41, Issue 11, Page(s) 3445–3453

Abstract: Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal ... ...

Abstract	Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-attention domain-adaptative segmentation network (DADASeg-Net) for cross-modality medical image segmentation. The key contribution of DADASeg-Net is a novel dual adversarial attention mechanism, which regularizes the domain adaptation module with two attention maps respectively from the space and class perspectives. Specifically, the spatial attention map guides the domain adaptation module to focus on regions that are challenging to align in adaptation. The class attention map encourages the domain adaptation module to capture class-specific instead of class-agnostic knowledge for distribution alignment. DADASeg-Net shows superior performance in two challenging medical image segmentation tasks.
MeSH term(s)	Neural Networks, Computer ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2022-10-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2022.3186698
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Article ; Online: Validity of Medical Insurance Guidelines for Orthognathic Surgery.

Schneider, Sydney A / Gateno, Jaime / Coppelson, Kevin B / English, Jeryl D / Xia, James J

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons

2020 Volume 79, Issue 3, Page(s) 672–684

Abstract: Purpose: The purpose of this study was to assess the validity of the medical insurance guidelines for orthognathic surgery used by the major American medical insurance companies.: Materials and methods: This study assessed the validity of the medical ...

Abstract	Purpose: The purpose of this study was to assess the validity of the medical insurance guidelines for orthognathic surgery used by the major American medical insurance companies. Materials and methods: This study assessed the validity of the medical insurance guidelines for orthognathic surgery used by Aetna, Anthem Blue Cross Blue Shield (BCBS), Cigna, Humana, and UnitedHealthcare (UHC). To evaluate the validity, we calculated the approval and denial rates of the 5 guidelines when we used them to assess the medical necessity for a control group of carefully selected patients. Patients were included in the control group if they met the criteria of a "prudent provider," crafted for this study. All rejected cases were analyzed to determine the root cause of the denials. The validity of the guidelines was also ascertained by determining their completeness and correctness. Results: The current study proves that no insurance guideline is in agreement with the criteria of a "prudent provider." When applied to carefully chosen patients, the requirements of BCBS, Aetna, Humana, and Cigna produce modest rejection rates of 6 to 12%. UHC is an outlier. Its guideline rejects 86% of patients, a rate about 7 times higher than its peers. Insurance guidelines disqualified patients for 3 different reasons: 1) no significant jaw deformity, 2) no demonstrable health impairment, and 3) the etiology of the condition is not a covered benefit. Additional evaluations demonstrate that the private insurance guidelines are incomplete, and at times, incorrect. Conclusions: This study shows that the medical insurance guidelines for orthognathic surgery used by the major American medical insurance plans need revision. The most consequential flaw was considering etiology in judging medical necessity. Fortunately, only one company adopted this policy. Moreover, all guidelines have omissions and errors in the way jaw deformity is determined and how health impairment is determined.
MeSH term(s)	Blue Cross Blue Shield Insurance Plans ; Humans ; Insurance ; Insurance, Health ; Orthognathic Surgery ; United States
Language	English
Publishing date	2020-11-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	392404-x
ISSN	1531-5053 ; 0278-2391
ISSN (online)	1531-5053
ISSN	0278-2391
DOI	10.1016/j.joms.2020.11.012
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Article ; Online: A Better Understanding of Unilateral Condylar Hyperplasia of the Mandible.

Gateno, Jaime / Coppelson, Kevin B / Kuang, Tianshu / Poliak, Cathy D / Xia, James J

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons

2020 Volume 79, Issue 5, Page(s) 1122–1132

Abstract: Purpose: Our current understanding of unilateral condylar hyperplasia (UCH) was put forth by Obwegeser. He hypothesized that UCH is 2 separate conditions: hemimandibular hyperplasia and hemimandibular elongation. This hypothesis was based on the ... ...

Abstract	Purpose: Our current understanding of unilateral condylar hyperplasia (UCH) was put forth by Obwegeser. He hypothesized that UCH is 2 separate conditions: hemimandibular hyperplasia and hemimandibular elongation. This hypothesis was based on the following 3 assumptions: 1) the direction of overgrowth, in UCH, is bimodal-vertical or horizontal, with rare cases growing obliquely; 2) UCH can expand a hemimandible with and without significant condylar enlargement; and 3) there is an association between the condylar expansion and the direction of overgrowth-minimal expansion resulting in horizontal growth and significant enlargement causing vertical displacement. The purpose of this study was to test these assumptions. Patients and methods: We analyzed the computed tomography scans of 40 patients with UCH. First, we used a Silverman Cluster analysis to determine how the direction of overgrowth is distributed in the UCH population. Next, we evaluated the relationship between hemimandibular overgrowth and condylar enlargement to confirm that overgrowth can occur independently of condylar expansion. Finally, we assessed the relationship between the degree of condylar enlargement and the direction of overgrowth to ascertain if condylar expansion determines the direction of growth. Results: Our first investigation demonstrates that the general impression that UCH is bimodal is wrong. The growth vectors in UCH are unimodally distributed, with the vast majority of cases growing diagonally. Our second investigation confirms the observation that UCH can expand a hemimandible with and without significant condylar enlargement. Our last investigation determined that in UCH, there is no association between the degree of condylar expansion and the direction of the overgrowth. Conclusions: The results of this study disprove the idea that UCH is 2 different conditions: hemimandibular hyperplasia and hemimandibular elongation. It also provides new insights about the pathophysiology of UCH.
MeSH term(s)	Facial Asymmetry/diagnostic imaging ; Facial Asymmetry/etiology ; Facial Asymmetry/pathology ; Humans ; Hyperplasia ; Hypertrophy/pathology ; Male ; Mandible/diagnostic imaging ; Mandible/pathology ; Mandibular Condyle/diagnostic imaging ; Mandibular Condyle/pathology
Language	English
Publishing date	2020-12-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	392404-x
ISSN	1531-5053 ; 0278-2391
ISSN (online)	1531-5053
ISSN	0278-2391
DOI	10.1016/j.joms.2020.12.034
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Article ; Online: Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.

Ma, Lei / Xiao, Deqiang / Kim, Daeseung / Lian, Chunfeng / Kuang, Tianshu / Liu, Qin / Deng, Hannah / Yang, Erkun / Liebschner, Michael A K / Gateno, Jaime / Xia, James J / Yap, Pew-Thian

IEEE transactions on medical imaging

2023 Volume 42, Issue 2, Page(s) 336–345

Abstract: Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in ...

Abstract	Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.
MeSH term(s)	Deep Learning ; Orthognathic Surgical Procedures/methods ; Computer Simulation ; Face/diagnostic imaging ; Face/surgery ; Orthognathic Surgery
Language	English
Publishing date	2023-02-02
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2022.3180078
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Article: DLLNet: An Attention-Based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models.

Lang, Yankun / Deng, Hannah H / Xiao, Deqiang / Lian, Chunfeng / Kuang, Tianshu / Gateno, Jaime / Yap, Pew-Thian / Xia, James J

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

2021 Volume 12904, Page(s) 478–487

Abstract: Dental landmark localization is a fundamental step to analyzing dental models in the planning of orthodontic or orthognathic surgery. However, current clinical practices require clinicians to manually digitize more than 60 landmarks on 3D dental models. ... ...

Abstract	Dental landmark localization is a fundamental step to analyzing dental models in the planning of orthodontic or orthognathic surgery. However, current clinical practices require clinicians to manually digitize more than 60 landmarks on 3D dental models. Automatic methods to detect landmarks can release clinicians from the tedious labor of manual annotation and improve localization accuracy. Most existing landmark detection methods fail to capture local geometric contexts, causing large errors and misdetections. We propose an end-to-end learning framework to automatically localize 68 landmarks on high-resolution dental surfaces. Our network hierarchically extracts multi-scale local contextual features along two paths: a landmark localization path and a landmark area-of-interest segmentation path. Higher-level features are learned by combining local-to-global features from the two paths by feature fusion to predict the landmark heatmap and the landmark area segmentation map. An attention mechanism is then applied to the two maps to refine the landmark position. We evaluated our framework on a real-patient dataset consisting of 77 high-resolution dental surfaces. Our approach achieves an average localization error of 0.42 mm, significantly outperforming related start-of-the-art methods.
Language	English
Publishing date	2021-09-21
Publishing country	Germany
Document type	Journal Article
DOI	10.1007/978-3-030-87202-1_46
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